End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points – We present a novel method for unsupervised neural architectures to encode and decode text into multiple time-space representations. The technique is based on a reinforcement learning algorithm and is evaluated on various real-world data sets. We demonstrate theoretically that the obtained temporal representations encode a rich syntactic dependency structure among the data, and encode a discriminative representation that learns to separate the syntactic dependency structure from the temporal structure. Our algorithm outperforms the state of the art baselines in both synthetic and real time.

We present a novel deep neural network architecture for predicting and ranking items by means of hierarchical matching pursuit. We propose a novel approach for ranking items by means of Hierarchical Matching Pursuit, which aims to approximate item ranking. Moreover, we extend our technique to the use of multi-task learning on a deep learning model. A Bayesian network is trained to predict the item ranking, using the rank correlation between items, using a discriminant model in order to obtain the rank correlation between items. We also propose a new method to rank items by means of hierarchical matching pursuit that aims for minimizing the gap between the items’ rank correlation. Experiments have shown that our method outperforms state-of-the-art ranking model prediction on two different datasets.

Learning A Comprehensive Classifier

A Novel Approach to Clustering with Noisy Nodes

End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points

Auxiliary Model Embedding for Constrained Constrained Object Localization

Learning Sparse Bayesian Networks with Hierarchical Matching PursuitWe present a novel deep neural network architecture for predicting and ranking items by means of hierarchical matching pursuit. We propose a novel approach for ranking items by means of Hierarchical Matching Pursuit, which aims to approximate item ranking. Moreover, we extend our technique to the use of multi-task learning on a deep learning model. A Bayesian network is trained to predict the item ranking, using the rank correlation between items, using a discriminant model in order to obtain the rank correlation between items. We also propose a new method to rank items by means of hierarchical matching pursuit that aims for minimizing the gap between the items’ rank correlation. Experiments have shown that our method outperforms state-of-the-art ranking model prediction on two different datasets.